Each AI platform draws on different sources and ranks brands differently. The Platforms page
(Analysis → Platforms) breaks every metric down by model so you can see where you’re strong and
where you’re invisible.
Matrix View
A heatmap of entities (rows) against AI models (columns), each cell showing that brand’s mention
rate on that platform. Rows are sortable by any column; your owned and primary entities sort first.
Scan for columns where a competitor is dark green and you are pale — that’s a platform-specific
content gap, not a general visibility problem.
A trend chart with four metric tabs, plus a ranked table underneath:
Share of Voice on this page is computed from raw mention counts per model, while the Overview
donut counts each brand once per response. The two numbers are both correct and will not match.
A tag × model heatmap — “project brand visibility per tag, broken down by model”. The icon toggle in
the card header switches between Regular tags and Response tags, and the card paginates
server-side (5, 25 or 50 rows per page).
Use it to find combinations rather than averages: a tag that performs fine overall can still be
invisible on one specific platform.
One card per model, each showing Mention rate, Avg. position, Share of Voice and the
number of runs tested. Click Show details to expand two tables:
- All Entities — Entity, Mention rate, Share of Voice, Avg. position
- All Sources — Source, Citation Rate, Results, Pages
Why it matters
Platforms differ in what they retrieve and what they trust:
- ChatGPT leans on a stable set of sites it treats as authoritative.
- Perplexity favours recent web content and cites heavily.
- Gemini and AI Overview reflect Google’s index.
- Claude weights different aspects of a brand’s description.
That makes platform-level reading actionable in a way an average never is: you can prioritise the
platforms your buyers use, tailor content to the sources a given platform actually cites, and catch
the moment a model update moves your numbers.